To fully understand the theoretical behaviour of our model we focused on two logging units both logged in 1996: one with a low logging intensity (1.1 m(^3)·ha(^{-1})) and one with a high logging intensity (33 m(^3)·ha(^{-1})).
Although positioning of SCD and OC became more variable now, we were still able to identify parameter sets that result in spatially confined and adjacent SCD and OC (Fig. 2), indicating that the anchoring distribution has no significant influence on the qualitative behaviour of our model.
To analyse the behaviour of our model with respect to the fluctuations in model parameters, we verified our model by performing sensitivity analysis for the rate constants.
We use Gillespie algorithms, also known as stochastic simulation algorithms (Gillespie, 1977), to explore the behaviour of our model of the bottlenecking process for a given parameterisation.
Therefore, we have validated the behaviour of our model with our own as well as published experimental data and have found supporting evidence for our predictions in the scientific literature.
We firstly validated the behaviour of our model against our own laboratory data of EGF stimulated ERK activation in PC12 cells and then used the model to investigate the effects of various cancerous alterations on the ERK signalling pathway; specifically, the effects of mutations in Ras, B-Raf and EGFR and also the effects of EGFR overexpression.
From these posterior processes, it can be seen that the mean behaviour of our model agrees with trajectories of underlying noiseless signals, and to make predictions about derivative processes, it is enough to consider only samples from the original sinusoidal trajectories.
The effects of reactor parameters including temperature and flow properties may be integrated into this model to predict the effects of various operating parameters and to optimise the design and behaviour of our reactor model.
In Figure 3, we studied the behaviour of our chemotherapy model on the basis of simplified chemotherapy actions.
